AAMJAF, Vol. 6, No. 1, 47–67, 2010

47

THREE-FACTOR CAPM RISK EXPOSURES:

SOME EVIDENCE FROM MALAYSIAN

COMMERCIAL BANKS

Aisyah Abdul Rahman

Faculty of Economics and Business,

Universiti Kebangsaan Malaysia,

43600, Bangi, Selangor

E-mail: eychah@ukm.my

ABSTRACT

This study investigates the determinants of the three-factor capital asset pricing model

(CAPM) risk exposures in the case of commercial banks. Five risk exposures are

examined namely, market, interest rate, exchange rate, total, and unsystematic risk

exposures. Our findings provide four major contributions. First, we find that different

types of risk exposures have different determinants. Second, the market risk exposure for

the Islamic bank in our study is lower than for conventional banks. Third, the merger

programme is fruitful because it reduces the interest rate risk exposure, total risk

exposure, and unsystematic risk exposure. Finally, our results show that the banks under

study have higher total and unsystematic risk exposures during the 1997 Asian financial

crisis. Thus, a clear understanding of this evidence helps in ensuring effective and

successful decision-making for regulators, policy makers and market players.

Keywords: banking, risk, merger, financial crisis

INTRODUCTION

Research found in banking literature has increasingly focused on risk exposure

since the 1997 Asian financial crisis. In fact, risk exposure has become one of the

most important factors in surviving the current, ongoing global economic crisis.

Although the scope of risk is vast, many studies focus on market, interest rate,

and foreign exchange rate risk exposure, as they are considered risks that can be

monitored, measured, and minimised with proper management. Among others,

one way to assess these risks is by using the three-factor capital asset pricing

model (CAPM), which evolved from the single-factor CAPM originally

developed by Sharpe (1964). Sharpe finds that the sensitivity to stock market

ASIAN ACADEMY of

MANAGEMENT JOURNAL

of ACCOUNTING

and FINANCE

Aisyah Abdul Rahman

48

return can determine asset pricing. However, Ross (1976) shows that stock

market return is not the only factor. Ross's arbitrage pricing theory (APT)

demonstrates that multiple factors significantly affect asset pricing. For the

banking sector, interest rate serves as the other established significant factor

(Stone, 1974; Martin & Keown, 1977; Brooth & Officer, 1985; Flannery &

James, 1984; Chance & Lane, 1980; Lynge & Zumwalt 1980; Jahankhani &

Lynge, 1980; Bae, 1990; and Lloyd & Shick, 1997). As financial globalisation

unfolds, a few studies have found that the exchange rate is another significant

factor (Chamberlain et al., 1997; Hahm, 2004; Francis & Hunter, 2004; Wong et

al., 2009; and Yong et al., 2009). Even though studies from developed countries

(Jianping & Saunders, 1995; Jianping & Ahyee, 1994; Allen et al., 1995; He et

al., 1996; and Lausberg, 2001) show that real estate investment return is another

factor, it is not significant for emerging countries, as real estate investment is still

at an early stage. Hence, market, interest rate, and exchange rate risk exposures

appear to be the significant factors in determining asset pricing for the Malaysian

banking sector.

It is important to take note that Malaysia applies a dual banking system

in which both conventional and Islamic banking products are offered to the same

customers. Hence, the customers are left with two choices in selecting which

banks they use to meet their banking needs. Their decisions may be influenced

by the risk exposures faced by the banks. To some extent, those risk exposures

depend on the background information of the banking systems. For the Islamic

banking industry, the first full-fledge Islamic bank was established in 1983,

known as Bank Islam Malaysia Bhd. (BIMB). It was given a monopoly status for

ten years to strengthen its position before the central bank allowed other Islamic

banks or windows to emerge in 1993. The Islamic banks are governed by the

Islamic Banking Act 1983 (IBA). As of January 2010, there are seventeen

Islamic banks, but only BIMB is listed in the Malaysia stock exchange.

Meanwhile, the conventional banking system began in Malaysia prior to its

independence and is governed by the Banking and Financial Institutions Act

1989 (BAFIA). In 1990, there were 22 local and 16 foreign banks altogether. In

order to encourage sustainability in the face of financial liberalisation, the

Malaysian central bank launched a consolidation programme in the late 1990s.

As of January 2010, there are 10 local and 13 foreign banks. Out of these, only

10 are listed in the stock exchange. As the estimation for CAPM risk requires

data on stock returns, this study uses data comprising all 11 bank-holding

companies listed in the stock exchange.

As burgeoning studies in this area centre on developed countries,

research on the Islamic banking industry is still relatively scarce. Most attention

is given either to conceptual frameworks or the macro-economic context. Many

studies simply presume that the behavioural attributes of Islamic banks are

Three-Factor CAPM Risk Exposures

49

similar to conventional banks. However, the fact that both banking systems have

different objectives, undergo different operational processes, and are bound by

different regulatory acts, it is unfair to generalise such findings. Indeed, Rahman

et al. (2009, 2008) show that the insolvency risk behaviour of Islamic banks is

different from conventional banks. Thus, the aim of this study is to investigate

the determinants of three-factor CAPM risk measures for Malaysian commercial

banks while at the same time acknowledging the different risk level of the Islamic

banking industry.

The contributions of this study are at least three-fold. Firstly, we evaluate

various risk exposures uniquely for the case of emerging markets like Malaysia.

Studying emerging markets is crucial, as there is no conclusive evidence

indicating the similarity of risk behaviour between emerging and developed

countries. On the one hand, Simpson (2002) finds that e-commerce risk,

efficiency, and rate of progress are significantly different between emerging and

developed countries. On the other hand, Joyce and Nabar (2009) show that global

economic crisis and financial liberalisation cause emerging economies to be

susceptible to the "sudden stops" on domestic investment due to the systemic

banking crisis from the developed countries, thereby suggesting similarities

between the two markets. In addition, as there are various types of bank risk

exposures (e.g., insolvency risk, subordinated debt implied risk, credit risk,

liquidity risk, and operational risk), we investigate the determinants of three-

factor CAPM risk namely, market risk, interest rate risk, foreign exchange rate

risk, unsystematic risk, and total risk exposure. Most prior CAPM research

examines the macro-economic factors affecting the asset pricing of the banking

industry, but it does not investigate the micro-economic features that may affect

the risk exposure of dynamic macro-economic factors. Finally, we also

investigate the different risk behaviour of the Islamic banking system.

The remainder of this paper is organised as follows: Section 2 outlines a

review of the literature on risk determinants, and section 3 highlights the data and

methodology. Section 4 reports the findings, and section 5 provides conclusion.

LITERATURE REVIEW

There are an enormous number of empirical studies on bank risk exposure.

Whereas Madura et al. (1994) and Ahmad and Ariff (2004) observe the

determinants of risk exposure per se, Saunders et al. (1990), Anderson and Fraser

(2000), Konishi and Yasuda (2004), Hassan (1993), Cebeyonan and Strahan

(2004), Gallo et al. (1996), Brewer et al. (1996), Gonzales (2004), Marco and

Fernandez (2008), Lepetit et al. (2008), and Yong, Faff and Chalmers (2009)

investigate various issues of bank risk exposure either in single-country or single-

Aisyah Abdul Rahman

50

region studies. There are some cross-country studies in this area, such as Dinger

(2009), Laeven and Levine (2009), and Angkinand and Wihlborg (2009).

Whereas single-country research includes bank-specific variables (BSV), cross-

country research often includes BSV and country-specific variables (CSV) as

control variables.

Madura et al. (1994) examine the determinants of the US-implied risk

exposure of deposit-taking institutions and commercial banks. Their findings for

depository institutions are not consistent with their findings for commercial banks.

With regards to depository institutions, real estate financing, real estate owned,

and level of capital buffer are the significant determinants. Meanwhile, for

commercial banks, the significant factors are real estate owned and capital buffer.

In another study, Ahmad & Ariff (2004) investigate the factors affecting the risk

exposure of Malaysian deposit-taking institutions using a single-factor CAPM.

Unlike Madura et al. (1994), they do not separately study commercial banks.

Their findings show that the determinants for market risk exposure include loan

default, cost of funds, loan expansion, and loan concentration. Meanwhile, the

determinants for unsystematic risk include loan default, cost of funds, and short-

term interest rate.

Studying how ownership structure affects US bank risk exposure,

Saunders et al. (1990) incorporate equity capital, fixed asset, and size as BSV.

They develop seven risk exposures based on a two-factor CAPM.1 Their findings

reveal that the BSV differently affect the seven types of risk exposure. One of

their interesting findings is that size is positively related to the market risk but

negatively related to the interest rate risk exposure. The underlying reason for

this conflicting result is that larger banks tend to be sensitive to market

movements, but at the same time, they are more able to diversify their interest

rate risk exposure.

Studying a similar issue, Anderson and Fraser (2000) estimate the single-

factor CAPM risk measure and insert an additional BSV, known as frequency.2

They argue that frequency represents the level of business risk exposure, as it

1 The risk measures include 1) total risk, 2) unsystematic risk for short-term interest rate, 3)

unsystematic risk for long-term interest rate, 4) market risk for short-term interest rate, 5) market

risk for long-term interest rate, 6) short-term interest rate, and 7) long-term interest rate risk

exposure.

The independent variables include 1) ownership structure (i.e., the proportion of stock held by

managers), 2) financial leverage [Capital/Total Asset (TA)], 3) operating leverage [Fixed

Asset/Total Asset (FA/TA)], and 4) size (TA). 2 The single-factor CAPM risk measures may be total risk, unsystematic risk, and systematic risk

exposures (i.e., total risk minus unsystematic risk exposures). Frequency is defined as the ratio of

an average daily share volume traded to number of shares outstanding.

Three-Factor CAPM Risk Exposures

51

denotes the speed at which new information is captured in the stock price and is

correlated to variances in bank balance-sheet and off-balance-sheet portfolios.

Their findings show that size is negatively related to total risk but positively

related to systematic risk; meanwhile, frequency is positively related to both total

and systematic risks.

Whereas Saunders et al. (1990) and Anderson and Fraser (2000) analyse

the case of US, Konishi and Yasuda (2004) and Marco and Fernandez (2008)

present comparable studies for the cases of Japan and Spanish, respectively.

Their studies add value to an existing gap in the literature with respect to risk

measurements.3 Whereas Konishi and Yasuda (2004) reveal that size and capital

buffer are significantly related to the two-factor CAPM and insolvency risk

exposures, Marco and Fernandez (2008) show that size, profitability, and

business types are significantly related to insolvency risk exposure. Regarding

cross-country research, Laeven and Levine (2009) and Angkinand and Wihlborg

(2009) find that size, credit quality, capital buffer, liquidity ratio, Gross

Domestic Product (GDP) growth, inflation, and interest rate are significantly

related to credit and insolvency risk exposures.

Examining the impact of mutual fund investments on US bank risk

exposure, Gallo et al. (1996) incorporate loan expansion, capital buffer, short-

term investment securities, and size as BSV. Estimating market and industry risk

exposures using the single-factor CAPM, they find that loan expansion is

significant for market and industry risk exposures, whereas short-term investment

securities are significant for industry risk exposure.

Looking into the impact of income structure on European banks risk

exposure, Lepetit et al. (2008) include size, capital buffer, profitability, loan

expansion, and cost of funds as BSV. Using the single-factor CAPM and

insolvency risk estimations, they find that size and capital buffer are significant

BSV.

Investigating how off-balance sheet activities affect bank risk, Hassan

(1993), Cebeyonan & Strahan (2004), and Yong et al. (2009) find that credit

expansion, credit quality, lending structure, cost of funds, Gap analysis, liquidity

ratio, capital buffer, and size are significant BSV. Hassan (1993) investigates the

3 Konishi and Yasuda (2004) analyse CAPM and insolvency risk whereas Marco and Fernandez

(2008) only focus on the insolvency risk exposure. The two-factor CAPM risk exposures include

1) total risk, 2) unsystematic risk for the short-term interest rate, 3) unsystematic risk for the

long-term interest rate, 4) market risk exposure for the short-term interest rate, 5) market risk

exposure for the long-term interest rate, 6) systematic risk exposure for the short-term interest

rate, and 7) systematic risk exposure for the long-term interest rate. The insolvency risk exposure

is the Z risk score.

Aisyah Abdul Rahman

52

impact of loan sales on US bank risk exposures based on the single-factor CAPM

and subordinated debt models.4 In contrast to Hassan (1993), Cebenoyan and

Strahan (2004) evaluate the loan sales-risk relationship based on financial ratios.5

Meanwhile, Yong et al. (2009) study the impact of derivative activities on risk

based on the three-factor CAPM approach.

DATA AND METHODOLOGY

This study adopts Generalised Least Squares (GLS) unbalanced panel regression

estimation. Three models are tested namely, pooled effect, fixed effect, and

random effect models. The best model is selected based on the Likelihood Ratio

and Hausman tests.6 To address heteroskedasticity, cross-section weighting is

used in the GLS estimation. Following Shahimi (2006) and Zakaria (2007), the

first-order autocorrelation problem is addressed based on the Park's model. The

data are comprised of all 11 listed commercial bank-holding companies in

Malaysia for 1994–2006. Table 1 displays the list of bank-holding companies, the

sampling period, and the completed merger period. The period from 2007 to 2009

are excluded due to the volatile market resulting from the food crisis, the oil price

crisis, and the US subprime crisis. The research design in this study is as follows:

it i it it it it itZ a I Y C (1)

Note that Z is the alternate measures of the three-factor CAPM risk exposures of

banks, X is a vector of BSV, I is a dummy for Islamic banking, Y are dummies for

the during and post-crisis periods, C is a dummy for the post-merger period, ai is

an individual-specific intercept, and β, γ, δ, and Φ are the slope coefficients to be

estimated.

The five alternate risk measures include market, interest rate, exchange

rate, total, and unsystematic risk exposures. (Detailed specifications of these risks

are discussed in the following subsection). We analyse the risk behaviour of the

Islamic banking industry using a dummy variable called DISLAM (Islamic bank

= 1; conventional banks = 0).7 We also take into account the financial crisis and

4 Please refer to Hassan (1993) for a detailed explanation of the implied asset subordinated debt

models. 5 The four risk measures are 1) ζROE,, 2) ζROA, 3) ζLLP./TL, and 4) ζnpl/TL. 6 Please refer to Beaver et al. (1989), Hsio (2002), Gujarati (2003), Shahimi (2006), and Zakaria

(2007) for further details on panel data regression techniques. 7 The Islamic bank's stock return is based on the stock prices of BIMB. As the majority of Islamic

banks are small and do not trade in Bursa Malaysia, BIMB is the only available sample

representing the Islamic banking industry in Malaysia. We cannot consider Islamic banking

Three-Factor CAPM Risk Exposures

53

consolidation effect during the period of study using the variables DCRISIS (year

1997 and 1998), DPOST-CRISIS (year 1999 to 2006), and DMERGER (post

merger = 1; pre-merger = 0). Most of the completed merger periods are either in

year 2000 or 2001, and thus, they do not overlap with the post-crisis period.

Table 1

List of Commercial Banks: Sampling Period and Completed Merger Period

Type of Bank Bank-Holding Company Sampling

Period

Completed

Merger Period1

Islamic bank Bank Islam Malaysia Bhd. (BIMB) 1994–2006 –

Conventional banks Hong Leong Bank Bhd. 1994–2006 2000

Malayan Banking Bhd. 1994–2006 2001

Public Bank Bhd. 1994–2006 2001

RHB Bhd. 2002

Affin Bank Bhd. 1994–2006 2000

Alliance Bank Bhd. 1994–2006 2001

Ambank Bhd. 1994–2006 2001

CIMB Bank Bhd. 1994–2006 2000

EON CAP Bhd. 1994–2006 2001

Southern Bank Bhd. 1994–2004 2001

Notes: 1Completed merger period is based on the final merger entity as announced in the annual reports of bank-

holding companies. See Said et al. (2008) for a detailed study of the evolution of banking groups and final entities after merger.

The selection of BSV in this study is closest in spirit to that of Hassan

(1993), Madura et al. (1994), Ahmad and Ariff (2004), Cebeyonan and Strahan

(2004), and Yong et al. (2009). (Appendix A presents a list of references that

have adopted similar variables).The BSV are TL, BPS, PLL, TE, GAP, INTEXP,

INV, LTA, NONII, and MGT. They are the best available proxies for loan

expansion, real estate lending, loan quality, capital buffer, GAP analysis, cost of

funds, liquid asset, size, non-interest income, and management efficiency,

respectively.8

windows and other Islamic subsidiaries of domestic bank-holding companies since they are not

listed in Bursa Malaysia, and hence, we do not have market prices. 8 Appendix A lists a number of studies that have employed similar BSV. We select the best

available variables for the case of Malaysia (that is, for a single-country study); thus,

insignificant variables and CSV are not included in the analysis. For the case of Islamic banks,

we let 1) interest expense be the sum of income distributed to depositor and shareholder fund and

2) non-interest income be the total income minus income from financing from both depositor and

shareholder funds.

Aisyah Abdul Rahman

54

The expected relationships among the BSV follow from previous studies.

For loan expansion, Hassan (1993), Madura et al. (1994), and Gallo et al. (1996)

suggest that the illiquidity of loan and the default risk exposure are the

underlying reasons for the positive relationship between TL and risk. For real

estate lending, Blasko and Sinkey Jr. (2006) show that real estate lending

increases the interest rate risk exposure, but Ahmad and Ariff (2004) find that

risky sector lending (the majority of which comes from the real estate sector)

reduces market risk exposure. Meanwhile, Rahman et al. (2008, 2009) show that

real estate lending reduces the insolvency risk exposure for Islamic banks, but the

reverse is true for conventional banks. Thus, the coefficient sign of BPS can be

negative or positive, depending on different types of risk exposures. With regards

to loan default, earlier studies hypothesise that PLL represents the probability of

future default and loan quality; thus, it is expected to be positively related to risk.

In the case of capital buffer, total equity is assumed to provide a cushion against

loss; hence, an increase in TE can reduce the bank risk exposure. For GAP

analysis, a positive GAP indicates that a particular bank is an asset-sensitive bank,

whereas a negative GAP indicates that it is a liability-sensitive bank. A positive-

GAP bank (that is, an asset-sensitive bank) is exposed to the risk that an interest

rate will fall, whereas a negative-GAP bank (that is, a liability-sensitive bank) is

exposed to the risk that an interest rate will increase. Thus, the greater the

absolute value of GAP is, the more the bank is exposed to changes in the interest

rate. Despite GAP analysis, Madura et al. (1994) argue that bank risk depends on

the proportion of funds obtained in the deposit account as measured by interest

expense, which is not captured in GAP analysis. They hypothesise that the higher

the deposit is, the higher the interest expense and the volatility of net interest

income are; thus, the bank is riskier. For the case of INV, it is linked to risk from

the perspective of deposit withdrawal. Maintaining idle cash is an opportunity

cost to banks, and thus, banks hold short-term investment securities to standby

the need for extraordinary deposit withdrawal, suggesting a negative association

between INV and risk. With regards to size, Saunders et al. (1990) and Hassan

(1993) argue that the greater the size, the greater the potential to diversify

business risk, thereby reducing bank risk exposure. However, Anderson and

Fraser (2000) suggest that the impact of size on risk depends on the lending

structure. If the loan composition is the same, larger banks should have lower risk

as compared to smaller banks. Nonetheless, if the loan structure is different, a

larger bank may have higher risk exposure than a smaller one due to its tendency

to embark into riskier lending sectors that can yield higher returns. Similarly,

Gonzales (2004) mentions that with the existence of the economy of scale,

increased market power, and "too big to fail" policies for big banks, a larger bank

tends to enter into risky activities either through lending strategies or off-balance

sheet activities. Against this background, size can be positively or negatively

related to risk exposure. For the case of non-financing activities, Madura et al.

(1994) offer evidence that diversifying from the traditional role banking (that is,

Three-Factor CAPM Risk Exposures

55

providing loans) reduces bank risk exposure. With regards to management

efficiency, Angbazo (1997) and Ahmad and Ariff (2003, 2004) believe that the

increasing efficiency of management reduces bank risk exposure. With regards to

Islamic banking, even though most of the Islamic banking research conceptually

highlights the additional risk exposure faced by the Islamic bank, this heightened

risk exposure has not yet been empirically tested. Considering this, DISLAM can

be positive or negative, depending on the development stage. For the crisis

dummies, risk exposures are expected to be higher during the crisis period but

may be higher or lower during the post-crisis period, depending on the recovery

process. Finally, if the benefit of bank consolidation indeed materialised,

DMERGER is expected to be negative.

SPECIFICATION FOR THREE-FACTOR CAPM RISK EXPOSURES

Our three-factor CAPM risk measure is akin to that employed by Hahm (2004),

Francis & Hunter (2004), Wong et al. (2009), and Yong et al. (2009). It is

expressed as follows:

Rt = α + βm (Rmt) + βi (Rit) + βforex (Rforex) + εt (2)

Note that:

Rt = daily stock return of bank-holding companies during a one-year period.

Rmt = daily Kuala Lumpur Composite Index (KLCI) market return during a

one-year period.

Rit = daily Malaysian Government Securities (MGS) 10-year return during a

one-year period.9

Rforext = daily nominal effective exchange rate (NEER) return during a one-year

period.

et = the error term that captures all other factors that affect bank return that

are not taken into account explicitly.

αj = the intercept of the characteristic line.

From the above equation, 5 yearly risk measures from 1994 to 2006 are estimated

for 11 Malaysian bank-holding companies. The five risk exposures are:

9 Daily return on 10-year MGS represents the long-term interest rate risk exposure during a one-

year period. We also analyse the short-term interest rate risk exposure using the overnight return

of Kuala Lumpur Interbank Offer Rate (KLIBOR), but our finding fails the F-statistic test. We

suspect that is due to very insignificant daily changes in the KLIBOR overnight rate. The results

for short-term interest rate risk exposure are provided upon request.

Aisyah Abdul Rahman

56

(i) Market risk exposure (βm)

(ii) Long-term interest rate risk exposure (βi)

(iii) Exchange rate risk exposure (βforex)

(iv) Unsystematic risk exposure (standard deviation of εt )

(v) Total risk exposure (standard deviation of Rt)

After the five risk measures are estimated, we then analyse our research design

based on Equation 1 using the GLS unbalanced panel regression estimation with

yearly data.

FINDINGS

The descriptive statistics as well as the correlation matrix are displayed in Tables

2 and 3, respectively. As the correlation matrix in Table 3 shows lower values

than 0.8, we consider that all independent variables are not seriously correlated;

hence, all variables are regressed simultaneously.10

For the regression results, the

fixed effect model appears to be the best model for all types of CAPM risk

exposures.11

Hence, the discussion in this section is based on the fixed effect

model. Table 4 presents the fixed effect estimations for market, long-term interest

rate, exchange rate, total, and unsystematic risk exposures.

For the case of market risk exposure, Table 4 shows that total loan

expansion (TL) is the only significant factor. The positive relationship of TL is as

expected and consistent with Hassan (1993), Gallo et al. (1996), Ahmad and

Ariff (2003, 2004), and Gonzales (2004). For DISLAM, our results show that the

Islamic banking system has lower risk exposure than the conventional banking

system. A lower market risk exposure for the Islamic banking industry may be

due to heavy reliance on the fixed profit rate system. Even though nowadays

there is another product innovation that offers a quite similar function to a floated

profit rate system (that is, the principal of Musyarakah Mutanakisah), its use in

the market is still new and limited, and thus, its influence on market risk exposure

is insignificant.

10 Gujarati (2003) sets a cut of point of 0.8 for the correlation coefficient matrix. Values higher than

0.8 indicate that the variables are strongly correlated; thus, they should be analysed to address

issues of multicollinearity. 11 The results for the no effect model, random effect model, the Likelihood ratio test, and the

Hausman test are provided upon request.

Table 2

Descriptive Statistics

Variables Mnemonics Mean Std. Dev

Risk Indicator (dependent variable)

Market Risk Exposure KLCI 1.077 0.404

Interest Rate Risk Exposure MGS 0.006 0.114

Exchange Rate Risk Exposure NEER 0.013 0.561

Total Risk Exposure TRISK 0.025 0.013

Unsystematic Risk Exposure UNSYS 0.019 0.008

Credit-related Variables

Ratio of Total Loans to Total Asset TL 0.520 0.250

Ratio of Broad Property Sector to Total Loan BPS 0.393 0.082

Ratio of Provision of Loan Loss to Total Asset PLL 0.009 0.010

Capital-related Variables

Ratio of Total Equity to Total Asset TE 0.085 0.037

Interest Rate-related Variables

Ratio of GAP to Total asset GAP –0.166 0.187

Ratio of Interest Expense to Total asset INTEXP 0.031 0.014

Liquidity-related Variable

Ratio of Short Term Investment to Total Asset INV 0.140 0.122

Business Operation-related Variables

Log of Total Asset LTA 7.318 0.440

Ratio of Non-interest Income to Total Asset NONI 0.009 0.006

Ratio of Earning Asset to Total Asset MGT 0.852 0.101

Table 3

Correlation Matrix of Independent Variables

(continued)

TL BPS PLL TE GAP INTEXP INV LTA NONII MGT

TL 1

BPS –0.040 1

PLL 0.502 –0.085 1

TE –0.076 0.426 –0.122 1

GAP 0.019 0.029 0.126 0.013 1

INTEXP 0.321 –0.154 0.540 –0.146 0.232 1

INV 0.309 0.022 0.231 –0.270 –0.255 0.230 1

LTA –0.276 –0.295 –0.185 –0.172 –0.322 –0.442 –0.054 1

Aisyah Abdul Rahman

58

Table 3 (continued)

For the case of long-term interest rate risk exposure, INTEXP and MGT show

significant effects. Consistent with prior studies, INTEXP is positively related to

interest rate risk. If a bank has a large amount of interest expenses, it is exposed

to greater interest rate movements, thus increasing its risk exposure. MGT is

negatively related to management efficiency, suggesting that banks are efficient

in managing their long-term interest rate risk exposure. For DISLAM, our result

shows that the risk behaviour of the Islamic banking industry is different from the

conventional banks. To the extent that Islamic banks benchmark their profit rate

to the market interest rate, their interest rate risk behaviour will always be similar

to that of conventional banks.

Table 4

The GLS Fixed Effect Estimation for the CAPM Risk Exposures

Expected

coefficient sign

Market

risk exposure

Interest

rate risk exposure5

Exchange

rate risk exposure

Total risk

exposure

Unsystematic

risk exposure

C

–1.772

(–1.069)

0.332

(0.876)

–2.869

(–0.415)

3.424***

(2.862)

2.362**

(2.088)

TL

+ 0.291*

(1.698)

0.044

(0.335)

–0.797

(–0.767)

0.001

(0.006)

–0.023

(–0.120)

BPS

+/– –0.045

(–0.294)

–0.174

(–0.992)

0.117

(0.120)

–0.035

(–0.166)

0.050

(0.263)

PLL

+ –0.032

(–0.721)

–2.170

(–1.658)

0.851**

(–2.258)

0.0178

(0.351)

0.019

(0.382)

TE

– –0.035

(–0.763)

0.254

(0.814)

–0.265

(–0.870)

–0.109**

(–2.605)

–0.097**

(–2.244)

GAP

+ –0.018

(–0.606)

–0.011

(–0.122)

–0.362**

(–2.162)

0.026

(1.042)

0.021

(0.982)

INTEXP

+ –0.082

(–0.728

8.186***

(3.158)

0.728

(1.363)

0.117

(1.284)

0.110

(1.282)

INV

– –0.047

(–1.415)

0.218

(1.121)

–0.278

(–1.384)

–0.081**

(–1.999)

–0.064

(–1.559)

TA

+/– 0.129

(0.539)

1.035

(0.602)

–1.201

(–1.512)

–1.119***

(–6.239)

–0.946***

(–5.551)

(continued)

TL BPS PLL TE GAP INTEXP INV LTA NONII MGT

NONII –0.342 –0.056 –0.309 0.071 0.133 –0.182 –0.260 –0.125 1

MGT 0.389 –0.017 0.189 –0.052 –0.184 0.249 0.214 –0.456 0.175 1

Three-Factor CAPM Risk Exposures

59

Table 4 (continued)

Expected

coefficient sign

Market

risk exposure

Interest

rate risk exposure5

Exchange

rate risk exposure

Total risk

exposure

Unsystematic

risk exposure

NONII

– 0.044

(0.738)

0.003

(0.126)

–0.849*

(–1.795

–0.078

(–1.420)

–0.062

(–1.310)

MGT

+/– –0.050

(–0.079)

–0.622**

(–2.143)

–1.623

(–0.392)

0.838

(1.598)

0.906*

(1.877)

DISLAM

+/– –0.372**

(–2.156)

–0.015

(–0.268)

–0.206

(–0.299)

–0.163

(–1.504)

–0.075

(–0.750)

DMERGER

– 0.033

(0.686)

–0.058*

(–1.751)

–0.184

(0.196)

–0.004*

(–1.889)

–0.004***

(–2.987)

DCRISIS

+ 0.211

(1.433)

–0.176

(0.404)

0.674***

(6.436)

0.477***

(4.730)

DPOST-CRISIS

+/– 0.113

(0.760)

–0.148

(0.476)

0.435***

(3.796)

0.330***

(3.152)

Adj R-squared 0.560 0.194 0.137 0.794 0.760

Prob (F-Stats) 0.000 0.021 0.029 0.000 0.000

D.W. statistics 1.980 1.832 2.603 1.911 1.978

Notes:

1. The dependent variables are market, long-term interest rate, exchange rate, total, and unsystematic risk exposures.

2. Figures in parentheses are t-statistics. 3. ***, **, and * denotes significance at 1%, 5%, and 10% confidence levels, respectively.

4. The findings for the GLS pool effect and GLS random effect models are available upon request.

With regards to exchange rate risk exposure, our result indicates that PLL, GAP,

and NONII are significant factors. The positive relationship of PLL implies that

as a bank increases its provision for loan losses, its exchange rate risk exposure

increases, suggesting that the Malaysian banks are vulnerable to external shock.

Note that increasing PLL implies decreasing loan quality. Decreasing loan

quality can be due to foreign borrowers who obtain loans denominated in foreign

currency or from local borrowers whose businesses are affected by the exchange

rate movement. The inverse relationship of GAP reveals that an increase in the

maturity mismatch of net short-term assets or liabilities is associated with a

decrease in exchange rate risk exposure. As GAP shows the net absolute position

of short-term assets and liabilities, a positive-GAP bank (that is, an asset-

sensitive bank) is exposed to the risk that the interest rate will fall, whereas a

negative-GAP bank (or a liability-sensitive bank) is exposed to the risk that the

interest rate will increase.12

During the period of study, the increasing

12

GAP = [net fed funds sold + trading account securities + securities maturing in less than one year

+ loan and leases maturing in less than one year + customer liabilities to the bank for outstanding

Aisyah Abdul Rahman

60

dependency of net short-term position on foreign denominated assets or liabilities

is beneficial, as it reduces exchange rate risk exposure. However, it should be

noted that risk indirectly depends on the accuracy in forecasting foreign-

denominated asset pricing; thus, banks should be aware of global economic

developments. Finally, the inverse relationship of NONII implies that the

increasing involvement of banks in fee-based activities reduces exchange rate

risk exposure, as most of the off-balance-sheet activities are self-liquidating

contingencies.

Regarding total risk exposure, TE, INV, and LTA are significant

variables. The negative relationships of TE and INV are as expected and

consistent to prior studies. Nonetheless, the inverse relationship of LTA implies

that as a bank increases its size, it reduces its total risk exposure. This finding

contradicts the results given by Saunders et al. (1990), Hassan (1993), and

Gonzalez (2004), but it is consistent with Anderson and Fraser (2000) and

Konishi and Yasuda (2004). Recall that total risk is a summation of systematic

(that is, market, interest rate, and exchange rate risk) and unsystematic risk. As

our findings show that LTA is not significant for systematic risk exposure but is

significant for unsystematic risk exposure. This implies that the impact of size on

the total risk exposure is greatly influenced by unsystematic risk exposure. This

inverse relationship implies that a larger Malaysian bank is more capable of

diversifying its unsystematic risk, which then reduces its total risk exposure.

For unsystematic risk exposure, the significant factors are TE, LTA, and

MGT. These variables are similar to total risk exposure, except for MGT. As total

risk exposure comprises both systematic (i.e. market, interest rate, and exchange

rate risk) and unsystematic risk, it can be inferred that the positive relationship

between MGT and unsystematic risk exposure is offset by the negative

relationship between MGT and interest rate risk exposure, which produces an

insignificant relationship between MGT and total risk exposure.

CONCLUSION

This study offers four contributions. First, as different types of risk exposure have

different risk determinants, market players should prioritise their risk

management based on their mission and vision. If a bank takes aggressive

strategies aimed at international activities, its main concern should be how to

efficiently manage the determinants of exchange rate risk exposure rather than

acceptances] – [domestic and foreign deposit less than one year + CDs less than one year + other

borrowed money + the bank liabilities on customer acceptances outstanding]. The absolute value

of GAP is deflated by total assets.

Three-Factor CAPM Risk Exposures

61

other types of risk exposure. Second, even though our findings show that the risk

behaviours of Islamic and conventional banking systems are similar for all types

of risk exposure except market risk exposure, it is worth mentioning that in the

upcoming years, the Islamic banking industry may behave differently as grows in

number and size. With the assumption that Islamic banks would eventually based

on practices different from those of conventional banks (particularly in

determining the profit rate return), we believe that the determinants of the interest

rate, total risk, and unsystematic risk exposures will differ greatly in the future.

Third, our findings show that bank mergers seem to benefit market players, as

they reduce interest rate, total, and unsystematic risk exposures. Finally, our

findings provide empirical evidence suggesting that the 1997 financial crisis

increased total and unsystematic risk exposures. With this in mind, policy makers

as well as market players in the banking industry should be better prepared to

face the ongoing global financial crisis by proactively introducing prudent risk

management guidelines to prevent the Malaysian banking system from repeating

the US subprime crisis.

REFERENCES

Ahmad, N. H., & Ahmad, S. N. (2004). Key factors influencing credit risk of Islamic

bank: A Malaysian case. Journal of Muamalat and Islamic Finance Research,

1(1), 65–80.

Ahmad, N. H., & Ariff, M. (2004). Key risk determinants of listed deposit-taking

institutions in Malaysia. Malaysian Management Journal, 8(1), 69–81.

———. (2003). What factors determine the total risk of deposit-taking institutions? Paper

presented in MFA's 5th Annual Symposium, 23–24 April, 888–902. Cyberjaya:

MMU.

Allen, M., Madura, T., & Jeff, W. K. J. (1995). Commercial banks exposure and

sensitivity to the real estate market. Journal of Real Estate Research, 10(2),

129–140.

Anderson, R. D., & Fraser, D. R. (2000). Corporate control, bank risk taking, and the

health of the banking industry. Journal of Banking and Finance, 24(8), 1383–

1398.

Angbazo, L. (1997). Commercial bank net interest margin, default risk, interest rate risk,

and off-balance sheet banking. Journal of Banking and Finance, 21(1), 55–87.

Angkinand, A., & Wihlborg, C. (2009). Deposit insurance coverage, ownship, and banks'

risk-taking in emerging markets. Journal of International Money and Finance,

29(2), 1–23. doi:10.1016/j.jimonfin.2009.08.001

Bae, S. C. (1990). Interest rate changes and common stock returns of financial institutions:

Revisited. The Journal of Financial Research, XIII(1), 71–79.

Beaver, W., Eger, c., Ryan, S., and Wolfson, M. (1989). Financial Reporting.

Supplemental disclosures and bank share prices. Journal of Accounting Research,

27, 15–178.

Aisyah Abdul Rahman

62

Blasko, M., & Sinkey Jr. J. F. (in press) Bank asset structure, real-estate lending, and risk

taking. The Quartely Review of Economic and Finance.

Brewer, E., William, E. J., & Thomas, S. M. (1996). Risk, regulation, and S & L

diversification into non-traditional assets. Journal of Banking and Finance,

20(4), 723–744.

Brewer, E. (1989). Relationship between bank holding company risk and non-bank

activity. Journal of Economics and Business, 41, 337–353.

Brooth, J. R., & Officer, D. T. (1985). Expectations, interest rates, and commercial bank

stocks. The Journal of Financial Research, XIII(1), 51–58.

Cebeyonan, A. S., & Strahan, P. E. (2004). Risk management, capital structure and

lending at banks. Journal of Banking and Finance, 28, 19–43.

Chamberlain, S., Howe, J., & Popper, H. (1997). The exchange rate exposure of US and

Japanese banking institutions. Journal of Banking and Finance, 21, 871–892.

Chance, D. M., & Lane, W. R. (1980). A re-examination of interest rate sensitivity in the

common stocks of financial institutions. Journal of Financial Research, 3(1),

49–56.

Dinger, V. (2009). Do foreign-owned banks effect banking system liquidity risk? Journal

of Comparative Economics, 41(2&3), doi: 10.1016/j.jce.2009.04.03

Flannery, M. J., & James, C. M. (1984). The effect of interest rate changes on the

common stock returns of financial institutions. The Journal of Finance, XXXIX

(4), 1141–1153.

Francis, B. B., & Hunter, D. M. (2004). The impact of the Europe on risk exposure of the

world's major banking industries. Journal of International Money and Finance,

23, 1011–1042.

Gallo, J. G., Apilado, V. P., & Kolari, J. W. (1996). Commercial bank mutual fund

activities: Implications for bank risk and profitability. Journal of Banking and

Finance, 20(5), 1775–1791.

Gonzales, F. (2004). Bank regulation and risk-taking incentives: An international

comparison of bank risk. Journal of Banking and Finance, 29, 1153–1184.

Gujarati, D. N. (2003). Basic Econometrics (4th ed.). Singapore: McGraw-Hill Higher

Education.

Hahm, J. H. (2004). Interest rate and exchange rate exposures of banking institutions in

pre-crisis Korea. Applied Economic, 36(13), 1409–1419.

Hassan, M. K. (1993). Capital market tests of risk exposure of loan sales activities of

large US commercial banks. Quarterly Journal of Business and Economics, 27–

49.

He, L. T., Myer, F. C. N., & Webb, J. R. (1996). The sensitivity of banks stock returns to

real estate. Journal of Real Estate Finance and Economics, 12(2), 203–220.

Hsio, C. (2002). Analysis of Panel Data (2nd ed.). NY: Cambridge University Press

Jahankhani, A., & Lynge, M. J. (1980). Commercial bank financial policies and their

impact on market-determined measures of risk. Journal of Financial Research,

11(3),169–78

Jianping, M., & Saunders, A. (1995), Bank risk and real estate: An asset pricing

perspective. Journal of Real Estate Finance and Economics, 10(3), 199–224.

Jianping, M., & Ahyee, L. (1994). Is there a real estate factor premium? Journal of Real

Estate Finance and Economics, 9(2), 113–126.

Three-Factor CAPM Risk Exposures

63

Joyce, J. P., & Nabar, M. (2009). Sudden stops, banking crises and investment collapses

in emerging market. Journal of Development Economics, 90(2), 314–322

Konishi, M., & Yasuda, Y. (2004). Factors affecting bank risk taking: Evidence from

Japan. Journal of Banking and Finance, 28(1), 215–232

Lausberg, C. (2001). The real estate market risk of banks: Evidence of its importance and

consequences for managing risk in real estate lending. Article submitted to the

2001 annual meeting of the European Financial Management Association.

Working paper series in SSRN.

Laeven, L., & Levine, R. (2009). Bank governance, regulation and risk-taking. Journal of

Financial economics, 93(2), 259–275.

Lepetit, L., Nys, E., Rous, P., & Tarazi, A. (2008). Bank income structure and risk: An

empirical analysis of European banks. Journal of Banking and Finance, 32(8),

1452–1467.

Lloyd, W. P., & Shick, R. A. (1997). A test of Stone's two-index model of returns.

Journal of Financial and Quantitative Analysis, 12, 731–741.

Lynge, M., & Zumwalt, K. (1980). An empirical study of the interest rate sensitivity of

commercial banks returns: A multi-factor approach. Journal of Financial

Research and Qualitative Analysis, 15, 731–742.

Madura, J., Martin, A. D., & Taylor, D. A. (1994). Determinants of implied risk of

depository institutions. Applied Financial Economics, 4(5), 363–370.

Marco, T. G., & Fernandez, M. D. R. (2008). Risk-taking behaviour and ownership in the

banking industry: The Spanish evidence. Journal of Economics and Business, 60,

332–354.

Martin, J. D., & Keown, A. J. (1977). Interest rate sensitivity and portfolio risk. Journal

of Financial and Quantitative Analysis, 12(6), 181–191.

Rahman, A., Ibrahim, M., & Meera, A. K. M. (2009). Lending structure and bank

insolvency risk: A comparative study between the Islamic and Conventional

Banks. Journal of Business & Policy Research, 4(2), 189–211.

———. (2008). Does the Islamic Bank lending structure matter? Paper presented in 3rd

Islamic banking, Accounting & finance (iBAF) Conference 2008. Financial

Intelligence in Wealth Management: Islam Hadhari's Perspective, 29–30th July

2008, The Legend Hotel, Kuala Lumpur.

Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory,

13(3), 341–60.

Said, M., Rasidah, M., Fauzias, N., Low, S. W., & A. Rahman, A. (2008). The efficiency

effects of mergers and acquisitions in Malaysian Banking Institutions. Asian

Journal of Business and Accounting, 1(1), 47–66.

Saunders, A., Strock, E., & Travlos, N. G. (1990). Ownership structure, deregulation and

bank risk taking. Journal of Finance, 45(2), 643–654.

Shahimi, S. (2006). Penentu margin pendapatan bersih Bank Islam. PhD Dissertation,

Universiti Kebangsaan Malaysia, 143.

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under

conditions of risk. Journal of Finance, 19(3), 425–442.

Simpson, J. (2002). The impact of the Internet in banking: Observations and evidence

from developed and emerging markets. Telematics and Informatics, 19(4), 315–

330

Aisyah Abdul Rahman

64

Stone, B. K. (1974). Systematic interest rate risk in a two-index model of returns. Journal

of Financial and Quantitative Analysis, 9(5), 709–721.

Wong, T. C., Wong, H. J., & Leung, P. (2009). The foreign exchange exposure of

Chinese Bank. China Economic Review, 20(2), 174–185.

Yong, H. H. A., Faff, R., & Chalmers, K. (2009). Derivative activities and Asia-Pacific

banks' interest rate and exchange rate exposures. Journal of International

Financial Markets, Institutions, and Money, 19(1), 16–32.

Zakaria, R. H. (2007). Banks' securitization involvement, bank lending and bank stability:

A panel data analysis. PhD Dissertation, Universiti Kebangsaan Malaysia, 130.

APPENDIX A

Independent Variables Adopted In Bank Risk Research

Author(s) and Publication Date Dependent Variables Independent Variables

1 Ahmad, N. H., & Ariff, M.

(2004). Key risk determinants

of listed deposit-taking

institutions in Malaysia.

Malaysian Management

Journal, 8(1), 6–81.

4 risk measures:

β = market risk

standard deviation of the

error term of the regression

equation = unsystematic

risk

standard deviation of bank

return = total risk

Ratio of Book Value to

Market Value of equity =

equity risk.

NPL/TL

MGT: as in Angbazo

LEV: Tier2/Total Capital

(data started in year 2002)

RISKY= (BPS + Purchase

of securities loan +

consumption credit loan)

Regulatory Cap: Tier 1/TL

(data started in year 2002)

Interest expense

PLL: Provision for Loan

Loss

RWA: Risk Weighted Asset

IBOR: Interbank rate

SPREAD: MGS rate- T-Bill

GAP: (RSA-RSL)/TA

Loan expansion:

Loan/Deposit:

Size: Log of TA

2 Madura, J., Martin, A. D., &

Taylor, D. A. (1994).

Determinants of implied risk of

depository institutions. Applied

Financial Economics, 4, 363–

370

The ex-ante risk: using

average daily implied

standard deviation (ISD) of

the jth bank in year t.

(followed Latane &

Rendleman (1976)

ISD = based on call option

price.

Disadvantage of ISD: since

it is implied, it may be

different to the actual risk of

the firm.

Loan Expansion: TL/TA

Real Estate Financing: RE

loan/TA

RE owned/TA (no data in

Malaysia)

Capital buffer: TE/TA

PLL: PLL/TA

GAP =(RSA/RSL)

Interest expense/TA

Non-interest income/TA

3 Saunders, A., Strock, E., &

Travlos, N. G. (1990).

Ownership structure,

deregulation and bank risk

taking. Journal of Finance.

45(2) June 643–654

7 risk measures:

o σs = tot risk

o σes =unsys for s/t

o σel= unsys for l/t

o βms = sys for s/t

o βml= sys for l/t

Ownership structure -

proportion of stock held by

managers – expected to be

(+) related to risk.

Capital Buffer: TE/TA

Operating Leverage –

o βis=sys for s/t

o βil= sys for l/t

FA/TA (in significant data

in Malaysia)

Size –log of TA

4 Hassan, M. Kabir. (1993).

Capital market tests of risk

exposure of loan sales activities

of large US commercial banks.

Quarterly Journal of Business

and Economics, 27–49

β= systematic risk

σ= standard deviation of

equity return

DRM (default Risk

Premium) of subordinated

debt

Bank Implied Asset Risk

(Ronn-Verma Option

Pricing Model)

Bank Implied asset Risk

(Gorton-Santomero debt

pricing method)

Loan sales: Loan sale/TA

(no data in Malaysia)

Capital buffer: TE/TA

Specialisation index/TA

Loan Quality: PLL/TA

GAP: (RSA-RSL)/TA

Size: Log of TA

Dividend Payout Ratio/TA

(no data in Malaysia)

5 Gallo, John G., Apilado,

Vincent P., & Kolari, James W.

(1996). Commercial bank

mutual fund activities:

Implications for bank risk and

profitability. Journal of Banking

and Finance, 20, 1775–1791

3 risk measurements:

β(market risk: Wilshire

5000 Index, systematic risk)

FINL(industry risk:

Wilshire finance Index,

systematic risk)

Standard deviation of the

error term of the regression

equation (unsystematic risk)

Investment securities/TA

Loan Expansion: TL/TA

(Sales Fed-purchased

Fed)/TA (insignificant in

Malaysia)

Capital buffer: TE/TA

Size: log of TA

Profitability: interest

income/TA

NONII: NONII/TA

FEES – Mutual fund

Fee/TA

6 Angbazo, Lazarus. (1997).

Commercial bank net interest

margin, default risk, interest

rate risk, and off-balance sheet

banking. Journal of Banking

and Finance, 21, 55–87.

NIM= Net Interest

Income/average earning

asset.

NII= interest income –

interest expense.

Loan Quality: NCO/TL (in

consistent NCO data in

Malaysia)

Liquid risk: Investment

securities/TL

Capital Buffer: (Tier 1+

Tier 2)/TA (data started in

year 2002)

Cost of funds: (non-interest

expense- non-interest

income) /earning asset

Non-interest bearing

reserve/TA (no data in

Malaysia)

Mgt efficiency: Earning

asset/TA

7 Anderson, Ronald D., & Fraser,

Donald R. (2000). Corporate

control, bank risk taking, and

the health of the banking

industry. Journal of Banking

and Finance, 24(8), 1383–1398

3 risk measures:

total risk (σSP)

unsystematic risk (σε)

systematic risk (total risk –

unsystematic risk)

Ownership variable (% of

shares held by unaffiliated

blockholders)

Outside-blockholders

Size: log of TA

Frequency: average daily

share volume

traded/number of shares

outstanding. (market data)

Tobin Q:

Σ(CSmv+Liabilitybv/TA)

(market data)

8 Cebeyonan, A. Sinan &

Strahan, Philip E. (2004). Risk

management, capital structure

and lending at banks. Journal of

Banking and Finance, 28, 19–

43

Use four financial ratio

measures:

σROE

σROA

σLLP./TL

σnpl/TL

Loan sales (no data and

insignificant in Malaysia)

Loan purchase (no data and

insignificant in Malaysia)

Capital buffer: TE/ Earning

asset

Liquid Asset: Investment

securities/TA

Commercial + industrial

loan) / TA

Real estate loan /TA

9 Gonzales, Francisco. (2004).

Bank regulation and risk-taking

incentives: An international

comparison of bank risk.

Journal of Banking and

Finance, 29, 1153–1184.

2 proxies:

total risk exposure:

σStockReturn

credit risk exposure:

NPL/TL

Size: log of TA

FA/TA (insignificant in

Malaysia)

Investment in

unconsolidated subsidiaries

(insignificant in Malaysia)

Liability: Total Debt/TA

Tobin's Q: (market data)

REG – annual index of

freedom in finance &

banking activities (no data

in Malasyia)

LAW and Order: annual

index (no data in Malaysia)

Legal origin (insignificant

in Malaysia)

10 Konishi, Masaru & Yasuda,

Yukihiro. (2004). Factors

affecting bank risk taking:

Evidence from Japan. Journal

of Banking and Finance. 28,

215–232

Risk measures:

total risk: (σSR)

unsystematic risk: (σerror )

systematic risk: (total risk -

unsystematic risk)

market risk: (β1)

interest rate risk: (β2)

Insolvency risk: (Z-score

developed by Boyd et al.

(1993)- using market data

Size – log of TA

Frequency: volume o

shares/number of shares

outstanding

Akumudari officers –

retired high rank ministry of

finance and bank of Japan

(not applicable in Malaysia)

Franchise value: (market

data)

Capital constraint: (dummy)

11 Marco, Teresa Garcia and

Fernandez, M. Dolores Robles.

(2008). Risk-taking behaviour

and ownership in the banking

industry: The Spanish evidence.

Journal of Economics and

Business, 60, 332–354

Z risk index Ownership structure:

(dummy)

Diversification: Herfindahl

index

Public control: (dummy)

Turnover of government

structure: (dummy)

Profitability: ROE

Loan Expansion: TL/TA

Size: Log of TA

12 Dinger, Valeriya. (2009) Do

foreign-owned banks affect

banking system liquidity risk?

Journal of Comparative

Economics, 41(2&3)

doi:10.1016/j.jce.2009.04.003

Liquidity risk Transnational: (dummy)

Size: log of TA

Capital buffer: TE/TA

Foreign Asset/TA

(insignificant in Malaysia)

Loan Expansion: TL/TA

Fiscal policy (cross-country

study)

GDP growth (cross-country

study)

Per capita GDP

(cross-country study)

13 Angkinand, Apanard, &

Wihlborg, Class. (2009)

Deposit insurance coverage,

ownership, and banks’ risk-

taking in emerging markets.

Journal of International Money

and Finance, 1–23.

Doi:10.1016/j.jimonfin.2009.08

.001

NPL/CAP

Std dev of NPL/CAP

Z-score

Capital buffer: TE/TA

Real GDP/Cap (cross-

country study)

Real GDP growth (cross-

country study)

M2/Reserve (cross-country

study)

Inflation(cross-country

study)

Real interest rate

(cross-country study)

14 Yong, Hue Hwa Au, Faff,

Robert., & Chalmers, Keryn.

(2009). Derivative activities and

Asia-Pacific banks' interest rate

and exchange rate exposures.

International Financial

Markets, Institutions and

Money, 19,

16–32.

Interest rate risk

Exchange rate risk

TDER – derivative/TA

(insignificant in Malaysia)

Size: log of TA

Capital buffer: TE/TA

liquid asset: Investment

securities/TA

Profitability: net interest

income/TA

NONII: NONII/Total

income

Loan expansion: TL/TA

PLL: PLL/TA

15 Laeven, Luc., & Levine, Ross.

(2009). Bank governance,

regulation and risk-taking.

Journal of Financial

economics, 93, 259–275

Z-score

Std deviation of ROA

Equity volatility

Earning volatility

Tobin's Q (market data)

Profitability: Revenue

growth

Per capita income (cross-

countries study)

Rights (no data in

Malaysia)

Capital buffer: TE/TA

Capital stringency (no data

in Malaysia)

Restrict (no data in

Malaysia)

DI: (dummy for insurance)